A proposed machine learning-based approach combining SVM, EEMD, CCA, WT, and HHO for EEG motion artifact detection and removal was superior to currently used algorithms.
Does the proposed machine learning and wavelet-based algorithm improve motion artifact detection and removal in single-channel EEG signals compared to currently used algorithms?
The proposed machine learning and wavelet-based algorithm provides superior motion artifact detection and removal in EEG signals compared to existing methods.
The electroencephalogram (EEG) signals are a big data which are frequently corrupted by motion artifacts. As human neural diseases, diagnosis and analysis need a robust neurological signal. Consequently, the EEG artifacts’ eradication is a vital step. In this research paper, the primary motion artifact is detected from a single-channel EEG signal using support vector machine (SVM) and preceded with further artifacts’ suppression. The signal features’ abstraction and further detection are done through ensemble empirical mode decomposition (EEMD) algorithm. Moreover, canonical correlation analysis (CCA) filtering approach is applied for motion artifact removal. Finally, leftover motion artifacts’ unpredictability is removed by applying wavelet transform (WT) algorithm. Finally, results are optimized by using Harris hawks optimization (HHO) algorithm. The results of the assessment confirm that the algorithm recommended is superior to the algorithms currently in use.
Stalin et al. (Thu,) conducted a other in EEG motion artifacts. Machine learning-based algorithm (SVM, EEMD, CCA, WT, HHO) vs. Currently used algorithms was evaluated on Motion artifact detection and removal. A proposed machine learning-based approach combining SVM, EEMD, CCA, WT, and HHO for EEG motion artifact detection and removal was superior to currently used algorithms.
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